This class is difficult, but you learn a lot from the class. This is one of the "make or break" classes for the major, with a touch of shared suffering, but ultimately is very rewarding. The professors and TAs are very helpful, and is the key reason why a lot of us was successful in the class. The professors and TAs care for our success, are readily available in their office hours and discussions, but you still need to put effort in to get a good grade in the class. Kao posts clear lecture notes for the class, and all past exams (with solutions) from previous years onto the class page. The TAs host 3+ hour long review sessions before the midterm and final and goes in depth on what would be on the exams. The homework assignments would consist of 4 to 5 problems (in the first half of the course, the fifth problem is a Python problem). This class provided me an excellent foundation, and showed me a different toolkit to solve problems as you understand from a high level about signals and learn about the frequency domain.

This is a interesting seminar that introduces you to the research of biomedical devices that interface directly with neurons (brain machine interfaces or BMIs), and you get to hear Kao discuss about his research as well. The workload is very light, lecture notes are posted on BruinLearn, and only has a final project (fairly easy to complete) assigned during the last two weeks of the quarter that for my year is completed in MATLAB (Kao has plans to update this to Python like he did for assignments for his ECE 102 class).

Kao is an amazing professor with a kind and open heart. Him and his TAs will lead you through the tough patches. But don't be fooled, this class is difficult. If you fall behind make sure you catch up ASAP because each lecture builds on the next and if you don't pace yourself you will end up breaking yourself.

Out of all the classes I've taken, Professor Kao is 100% the best lecturer in the department. While the class is pretty difficult, he is able to break down the intuition required to understand the material. He also gives plenty of extra material to practice, releasing exams from previous years, original and annotated lecture notes, etc. This class does require a lot of time to do well in as you are assigned 7 HW assignments, each of which take multiple hours to complete. Professor Kao is really approachable and really cares about teaching. The exams are difficult, but because he teaches the material well, have pretty high medians/averages. The TAs for the class were also amazing.

I was not going to take this class, but I sat in on his first lecture and was captivated by him. He is genuinely a great lecturer, and it shows in his ratings. He and the TAs work really hard to make this class easy to understand and give you a solid foundation that other professors might not provide. There is no one better to take this class with.

If you can, would absolutely recommend taking the class with Kao - he is hands down the best lecturer I've had at UCLA until now! His lectures are clear and interesting, and have a good balance between presenting new content and doing examples, leading to you actually understanding complex stuff like convolution. If you're still confused, he has office hours multiple times a week and is responsive on email.

As other reviews said, the class material itself isn't easy. Homeworks take hours, which he warns you about. The difficulty helps you learn and prepares you for the exams, and all other aspects of the class support you. The exams themselves are fine and had high averages - spend some time reviewing the review sessions the TAs host, which almost mirror the actual exams.

Overall, would VERY highly recommend! Thanks Professor Kao and Rakshith!

Hands down my favorite class I've taken at UCLA so far. A mix of neuroscience, signal processing, and ML, it really teaches a lot of useful skills that can be used in a lot of diffferent fields of EE and CS. Homeworks are hard and primarily math focused or coding in python and numpy focused, and are *very* time intensive, don't start too late! Kao's 4 late days policy saved me a couple times for sure though during the quarter. Tests are also a doozy, but shoutout to the TA Tonmoy for his goated review sessions and Kao for providing all the previous midterms/finals to help you study.

A lot of the tools that we've used in this class: numpy, signal processing/filtering, clustering, have come up for me already in EE research and throughout CS internships so this class has definitely helped a lot in terms of experience that can be used in industry. The class material itself is extremely interesting to me as well so I'd definitely recommend it!

This has been my favorite class. If you're interested in neuroscience, machine learning, signal processing, brain-machine interfaces, or if you just need an interesting EE elective, this is the class for you. Most of the class was EE majors, but the course is incredibly useful for anyone interested in neuroscience and engineering. Professor Kao is one of the best instructors I have ever had: the lectures were very engaging and clear, the homeworks were well-written, the exams were fair, and the office hours were helpful.

The first third of the class focuses on basic neuroscience, which requires no prior background. The last 2 thirds focus on modeling neuron activity and then turning brain activity into actions in real life (i.e. moving a cursor on a screen or something similar). In order to do well in the class, you just need to be decent at calculus, linear algebra, Python programming, and probability theory (it will be very difficult to succeed without these prerequisites). The homeworks can be tricky, so do go to Jonathan's or any of the TAs' office hours since they are also very helpful. Most of them included a Python (Jupyter notebook) component which made them slightly time-consuming, but still very doable (and fun!).

Almost everyone I met in the class just took it because of Professor Kao's teaching and lecture style (annotating detailed lecture slides from an iPad), and walked away with an appreciation for the field.

I really enjoyed this class with Kao. The concepts are really interesting, and he does a great job of explaining intuition and applications (including to his own research). He is very thorough and doesn't go too fast, and treats the subject with about as much mathematical rigor as you can without being in an actual math class.
He created slides in advance, then annotated them in class and posted the annotated PDFs for us to download. I never had to refer to the textbook to understand the material.

Kao is also very accommodating to students. He will move office hours, make corrections to materials, email quickly, and is always happy to look up the answer to a question if he does not know the answer already.

This class with Kao, I would rank as one of the top I've taken at UCLA. This was the first time he taught signals and systems, but he came into class very well prepared every single day. He really cared about making sure everyone understands the material best they can by giving a lot of intuition to the things he's teaching, and he mentioned how when he was an undergrad he did badly on the first midterm due to a lack of intuitive understanding. He also tends to mention how he uses the tools in 102 in his own work in neural prostheses, which is actually super cool to hear about.

There was a small pacing problem near the beginning of the class, because he would answer everyone's question in detail even though that's literally the next slide, but he later corrected that issue. Homework was challenging but doable – which to be honest is what should be the case. The grading curve was very lenient and he is committed to give everyone as high a grade as possible.

Basically, Kao is the best professor to take 102 with.